It then proved surprisingly impressive at analysing images of galaxies from the Hubble Space Telescope.

David Koo, professor emeritus of astronomy and astrophysics at UC Santa Cruz said: "This is the beginning of a very exciting time for using advanced artificial intelligence in astronomy.”

NC

BLUE NUGGET: Galaxy evolution can be seen across three phases

Last footsteps on the Moon: Stunning resurfaced images of Apollo 17

Incredible images of the last Apollo mission 17 back in 1972.

1 / 17

Mediadrumworld

Apollo 17 prepares for take-off on December 7 1972

“This technique can be applied to the classification of other astrophysical phenomena”

David Koo

The study, published in Astrophysical Journal, read: ”We use machine learning to identify in colour images of high-red shift galaxies an astrophysical phenomenon predicted by cosmological simulations."

This phenomenon, called the Blue Nugget phase, is the compact star-forming phase in the central regions of many growing galaxies.

The AI uses the algorithm to train itself how to identify different phases of galaxy evolution using mock images and was then applied to Hubble Telescope images.

“We show that BNs are identified by the computer within a time window of ∼ 0.15 Hubble times.

“This technique can be applied to the classification of other astrophysical phenomena.

SpaceX launches world's most POWERFUL rocket to fire car into orbit

SpaceX, founded by Elon Musk has launched its Falcon Heavy rocket, the most powerful rocket in the world. As part of its payload the Falcon Heavy is carrying Musk’s cherry red Roadster from Tesla, his electric car company.

“It could improve comparison of theory and observations in the era of large imaging surveys and cosmological simulations.”

The facial recognition tech’s findings are exciting because they suggest the deep learning algorithm is identifying a pattern happening in real galaxies on its own.

The researchers used state-of-the-art galaxy simulations developed by Primack and an international team of collaborators ran the simulations and led interpretation.

"This project was just one of several ideas we had," Koo said.

"We wanted to pick a process that theorists can define clearly based on the simulations, and that has something to do with how a galaxy looks, then have the deep learning algorithm look for it in the observations.

“We're just beginning to explore this new way of doing research. It's a new way of melding theory and observations."

Koo also explained that deep learning has the potential to reveal new aspects of space humans can’t see.

"We want to do a lot more testing of this approach, but in this proof-of-concept study, the machine seemed to successfully find in the data the different stages of galaxy evolution identified in the simulations."

The algorithm is like a “black box” however, meaning it’s extremely difficult to discern which features in the data the machine is using to make its classifications.

Network interrogation techniques can identify which pixels in an image contributed most to the classification, however, and the researchers tested one such method on their network.